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Major winning Convolutional Neural Networks (CNNs), such as AlexNet, VGGNet,ResNet, GoogleNet, include tens to hundreds of millions of parameters, whichimpose considerable computation and memory overhead. This limits theirpractical use for training, optimization and memory efficiency. On thecontrary, light-weight architectures, being proposed to address this issue,mainly suffer from low accuracy. These inefficiencies mostly stem fromfollowing an ad hoc procedure. We propose a simple architecture, calledSimpleNet, based on a set of designing principles, with which we empiricallyshow, a well-crafted yet simple and reasonably deep architecture can perform onpar with deeper and more complex architectures. SimpleNet provides a goodtradeoff between the computation/memory efficiency and the accuracy. Our simple13-layer architecture outperforms most of the deeper and complex architecturesto date such as VGGNet, ResNet, and GoogleNet on several well-known benchmarkswhile having 2 to 25 times fewer number of parameters and operations. Thismakes it very handy for embedded systems or systems with computational andmemory limitations. We achieved state-of-the-art result on CIFAR10outperforming several heavier architectures, near state of the art on MNIST andcompetitive results on CIFAR100 and SVHN. We also outperformed the much largerand deeper architectures such as VGGNet and popular variants of ResNets amongothers on the ImageNet dataset. Models are made available at:https://github.com/Coderx7/SimpleNet

Lets keep it simple, Using simple architectures to outperform deeper and more complex architectures